Death of the Data Scientist?

The importance and potential applications of unlocking "Big Data" have transcended political, geographical and industrial boundaries. Yet, for many enterprises, the benefits of understanding their data remains elusive. In part, this is caused by a fundamental shortage of candidates.

The role of a Data Scientist is multifaceted and requires both a broad skill set and no small amount of education. They need to demonstrate mathematical acumen, the ability to manage huge quantities of raw data, knowledge of machine learning, as well as proficiency in analytical tools, programming languages and, perhaps most importantly, to draw upon these aspects to spot anomalies and trends in order to facilitate actionable business insights. Furthermore, they are expected to do all this while making it relatable and understandable to the layman (or executives!). Given their expertise and their short supply, it is little wonder that Data Scientists have been able to command such attractive salaries and that "Data Scientist" should be perceived as the sexiest, most zeitgeisty contemporary job title. However, could all this be about to change?

For some, the answer to this question is a resounding, "yes" and they point to the irresistible rise of the machines as evidence of this fact. From Siri to Alexa, or the victory of AlphaGo over grandmaster Lee Seedol, it can be seen that AI is developing at an unprecedented rate, becoming ever more viable and perhaps, increasingly and fiendishly clever. According to a 2017 report by the RSA, 4 million UK private sector jobs could be replaced in the next decade by AI and the finance, accounting, transportation, distribution, marketing and advertising sectors are amongst those most likely to to be affected. For Data Scientists, the concomitant factors of developments in AI and their own relative scarcity are forcing companies to look ever more seriously into the benefits of artificial intelligence. Consequently, some now argue that AI, rather than plugging the skills gap, may actually come to kill off the need for Data Scientists completely.

There are some undeniable benefits to AI. For example, a machine learning algorithm can work through large quantities of data much faster than even the most dedicated human nerd can. Furthermore, these algorithms have the potential to analyse the data and provide similar actionable information to that of a Data Scientist. The development of Natural Language Processing (NLP) is making AI ever easier to communicate with and utilise for specific functions. While Iron Man's J.A.R.V.I.S is still some way away, NLP is allowing users without a specific data science skillet to manipulate and build visualisations themselves and convert them into their own business intelligence solutions.

While demand for Data Scientist remains high at present, perhaps the march of machine progress will send them the way of the Dodo bird.